IBM's Big SQL is their SQL for Hadoop product that allows users to run SQL queries on Hadoop data. It uses the Hive metastore to catalog table definitions and shares data logic with Hive. Big SQL is architected for high performance with a massively parallel processing (MPP) runtime and runs directly on the Hadoop cluster with no proprietary storage formats required. The document compares Big SQL to other SQL on Hadoop solutions and outlines its performance and architectural advantages.
Hadoop-DS: Which SQL-on-Hadoop Rules the HerdIBM Analytics
Originally Published on Oct 27, 2014
An overview of IBM's audited Hadoop-DS comparing IBM Big SQL, Cloudera Impala and Hortonworks Hive for performance and SQL compatibility. For more information, visit: http://www-01.ibm.com/software/data/infosphere/hadoop/
Using your DB2 SQL Skills with Hadoop and SparkCynthia Saracco
Learn about Big SQL, IBM's SQL interface for Apache Hadoop based on DB2's query engine. We'll walk through some code example and discuss Spark integration for JDBC data sources (DB2 and Big SQL) using examples from a hands-on lab. Explore benchmark results comparing Big SQL and Spark SQL at 100TB. This presentation was created for the DB2 LUW TRIDEX Users Group meeting in NYC in June 2017.
The document summarizes several popular options for SQL on Hadoop including Hive, SparkSQL, Drill, HAWQ, Phoenix, Trafodion, and Splice Machine. Each option is reviewed in terms of key features, architecture, usage patterns, and strengths/limitations. While all aim to enable SQL querying of Hadoop data, they differ in support for transactions, latency, data types, and whether they are native to Hadoop or require separate processes. Hive and SparkSQL are best for batch jobs while Drill, HAWQ and Splice Machine provide lower latency but with different integration models and capabilities.
Big Data: Big SQL web tooling (Data Server Manager) self-study labCynthia Saracco
This hands-on lab introduces you to Data Server Manager, a Web tool for querying and monitoring your Big SQL database. Data Server Manager (DSM) and Big SQL support select Apache Hadoop platforms.
SQL on Hadoop
Looking for the correct tool for your SQL-on-Hadoop use case?
There is a long list of alternatives to choose from; how to select the correct tool?
The tool selection is always based on use case requirements.
Read more on alternatives and our recommendations.
Getting started with Hadoop on the Cloud with BluemixNicolas Morales
Silicon Valley Code Camp -- October 11, 2014.
Session: Getting started with Hadoop on the Cloud.
Hadoop and Cloud is an almost perfect marriage. Hadoop is a distributed computing framework that leverages a cluster built on commodity hardware. The Cloud simplifies provisioning of machines and software. Getting started with Hadoop on the Cloud makes it simple to provision your environment quickly and actually get started using Hadoop. IBM Bluemix has democratized Hadoop for the masses! This session will provide a brief introduction to what Hadoop is, how does cloud work and will then focus on how to get started via a series of demos. We will conclude with a discussion around the tutorials and public datasets - all of the tools needed to get you started quickly.
Learn more about BigInsights for Hadoop: https://developer.ibm.com/hadoop/
This document discusses Tableau's role in big data architectures and its integration with Hadoop. It outlines different workload categories for business intelligence and their considerations for Tableau. Three integration models are described: isolated exploration, live interactive query, and integrated advanced analytics. Capability models are presented for each integration approach regarding suitability for Hadoop. Finally, architecture patterns are shown for isolated exploration, live interactive querying, and an integrated advanced analytics platform using Tableau and Hadoop.
SQL Server 2016 introduces new features for business intelligence and reporting. PolyBase allows querying data across SQL Server and Hadoop using T-SQL. Integration Services has improved support for AlwaysOn availability groups and incremental package deployment. Reporting Services adds HTML5 rendering, PowerPoint export, and the ability to pin report items to Power BI dashboards. Mobile Report Publisher enables developing and publishing mobile reports.
The Cloudera Impala project is pioneering the next generation of Hadoop capabilities: the convergence of interactive SQL queries with the capacity, scalability, and flexibility of a Hadoop cluster. In this webinar, join Cloudera and MicroStrategy to learn how Impala works, how it is uniquely architected to provide an interactive SQL experience native to Hadoop, and how you can leverage the power of MicroStrategy 9.3.1 to easily tap into more data and make new discoveries.
The document summarizes new features in SQL Server 2016 SP1, organized into three categories: performance enhancements, security improvements, and hybrid data capabilities. It highlights key features such as in-memory technologies for faster queries, always encrypted for data security, and PolyBase for querying relational and non-relational data. New editions like Express and Standard provide more built-in capabilities. The document also reviews SQL Server 2016 SP1 features by edition, showing advanced features are now more accessible across more editions.
Adding Value to HBase with IBM InfoSphere BigInsights and BigSQLPiotr Pruski
This is the extended deck I used for my presentation at the Information On Demand 2013 conference for Session Number 1687 - Adding Value to HBase with IBM InfoSphere BigInsights and BigSQL.
This presentation covers accessing HBase using Big SQL. It starts by going over general HBase concepts, than delves into how Big SQL adds an SQL layer on top of HBase (via HBase storage handler), secondary index support, queries, etc.
Big Data: Explore Hadoop and BigInsights self-study labCynthia Saracco
Want a quick tour of Apache Hadoop and InfoSphere BigInsights (IBM's Hadoop distribution)? Follow this self-study lab to get hands-on experience with HDFS, MapReduce jobs, BigSheets, Big SQL, and more. This lab was tested against the free BigInsights Quick Start Edition 3.0 VMware image.
Oracle Warehouse Builder is Oracle's tool for designing, deploying, and managing business intelligence and data integration projects on the Oracle database. It provides a graphical environment to extract, transform, and load data from various sources into a Oracle data warehouse or datamarts. Warehouse Builder manages the full lifecycle of metadata and data, and enables users to design and deploy ETL processes, reporting infrastructure, and manage the target schema.
The document discusses NoSQL databases and Oracle's NoSQL Database product. It outlines key features of Oracle NoSQL Database including its scalability, high availability, elastic configuration, ACID transactions, and commercial support. Benchmark results show Oracle NoSQL Database can achieve over 1 million operations per second and scale linearly with additional servers. The document also provides information on licensing and support options for Oracle NoSQL Database Community Edition and Enterprise Edition.
SQL on Hadoop: Defining the New Generation of Analytic SQL DatabasesOReillyStrata
The document summarizes Carl Steinbach's presentation on SQL on Hadoop. It discusses how earlier systems like Hive had limitations for analytics workloads due to using MapReduce. A new architecture runs PostgreSQL on worker nodes co-located with HDFS data to enable push-down query processing for better performance. Citus Data's CitusDB product was presented as an example of this architecture, allowing SQL queries to efficiently analyze petabytes of data stored in HDFS.
This document provides an introduction to Microsoft Azure DocumentDB. It discusses how DocumentDB is a non-relational or NoSQL database that stores data in JSON documents. It also overview how DocumentDB provides scalability, high availability, and fast performance for large document workloads. Key features of DocumentDB discussed include its resource and interaction models, indexing, consistency options, querying capabilities, and support for JavaScript transactions.
The document discusses Impala SQL support, including that Impala can access tables defined through Impala or Hive DDL, supports similar DML statements to HiveQL, and provides many built-in functions with the same names and parameters as HiveQL equivalents. It covers Impala's support for data definition languages (DDL) like CREATE TABLE and ALTER TABLE, data manipulation languages (DML) like INSERT and LOAD DATA statements, and Impala-specific functions and languages (ISL).
Tempto is a product test framework that allows developers to write and execute tests for SQL databases running on Hadoop. Individual test requirements such as data generation, HDFS file copy/storage of generated data and schema creation are expressed declaratively and are automatically fulfilled by the framework. Developers can write tests using Java (using a TestNG like paradigm and AssertJ style assertion) or by providing query files with expected results. We will show how we use it for presto product tests.
Benchto is a benchmark framework that provides an easy and manageable way to define, run and analyze macro benchmarks in clustered environment. Understanding behavior of distributed systems is hard and requires good visibility intostate of the cluster and internals of tested system. This project was developed for repeatable benchmarking ofHadoop SQL engines, most importantly Presto.
Presentation on Presto (http://prestodb.io) basics, design and Teradata's open source involvement. Presented on Sept 24th 2015 by Wojciech Biela and Łukasz Osipiuk at the #20 Warsaw Hadoop User Group meetup http://www.meetup.com/warsaw-hug/events/224872317
Which Hadoop Distribution to use: Apache, Cloudera, MapR or HortonWorks?Edureka!
This document discusses various Hadoop distributions and how to choose between them. It introduces Apache Hadoop and describes popular distributions from Cloudera, Hortonworks, and MapR. Cloudera is based on open source Hadoop but adds proprietary tools, while Hortonworks uses only open source software. MapR takes a different approach than Hadoop with its own file system. The document advises trying different distributions' community editions to compare them and determine features needed before selecting a distribution.
Presented at the Auckland AWS Meet-up:
In this meet-up, Chris will take us through an interactive session that will examine log solutions in the cloud.
We'll take a look at some possible build-your-own architectures on AWS, common tools and practices, and commercial options. We'll then demo logging data from an EC2 Instance using Amazon Kinesis, Amazon Elasticsearch Service and S3.
Next Generation Hadoop: High Availability for YARN Arinto Murdopo
The document proposes a new architecture for YARN to solve its availability limitation of single-point-of-failure in the resource manager. The key aspects of the proposed architecture are:
1. It utilizes a stateless failure model where all necessary states and information used by the resource manager are stored in a persistent storage.
2. MySQL Cluster (NDB) is proposed as the storage technology due to its high availability, linear scalability, and high throughput of up to 1.8 million writes per second.
3. A proof-of-concept implementation was done using NDB to store application states and their corresponding attempts. Evaluations showed the architecture is able to increase YARN's availability and NDB
Project presentation for High Availability in YARN project. We propose to use MySQL Cluster (NDB) to tackle High Availability issue in YARN. We also developed benchmark framework to investigate whether MySQL Cluster (NDB) is better than Apache's proposed storage (ZooKeeper and HDFS)
Full project report will be uploaded after I finish it.
Hello, Enterprise! Meet Presto. (Presto Boston Meetup 10062015)Matt Fuller
Teradata has been hard at work on Presto, and we want to share with you what we've done so far and our roadmap going forward. From presto-admin, a tool for installing and administering Presto, to YARN/Ambari support, to fully certified JDBC and ODBC drivers, we are committed to making Presto the best, most enterprise-ready SQL-on Hadoop solution out there.
Prestogres is a PostgreSQL protocol gateway for Presto that allows Presto to be queried using standard BI tools through ODBC/JDBC. It works by rewriting queries at the pgpool-II middleware layer and executing the rewritten queries on Presto using PL/Python functions. This allows Presto to integrate with the existing BI tool ecosystem while avoiding the complexity of implementing the full PostgreSQL protocol. Key aspects of the Prestogres implementation include faking PostgreSQL system catalogs, handling multi-statement queries and errors, and security definition. Future work items include better supporting SQL syntax like casts and temporary tables.
Hadoop World 2011: Big Data Architecture: Integrating Hadoop with Other Enter...Cloudera, Inc.
Recent research has pointed out the complementary nature of Hadoop and other data management solutions and the importance of leveraging existing systems, SQL, engineering, and operational skills, as well as incorporating novel uses of MapReduce to improve analytic processing. Come to this session to learn how companies optimize the use of Hadoop with other enterprise systems to improve overall analytical throughput and build new data-driven products. This session covers: ways to achieve high-performance integration between Hadoop and relational-based systems; Hadoop+NoSQL vs Hadoop+SQL architectures; high-speed, massively parallel data transfer to analytical platforms that can aggregate web log data with granular fact data; and strategies for freeing up capacity for more explorative, iterative analytics and ad hoc queries.
Presto as a Service - Tips for operation and monitoringTaro L. Saito
- Presto as a Service in Treasure Data involves deploying Presto using blue-green deployments with no downtime and automatic error recovery of failed queries.
- Monitoring Presto involves using its JSON API to view queries and query plans as well as collecting Presto metrics with Fluentd and detecting anomalies.
- Benchmarking compares query performance between Presto versions by running predefined query sets and aggregating the results.
Hybrid Data Architecture: Integrating Hadoop with a Data WarehouseDataWorks Summit
Mather Economics wanted a data architecture that could integrate Hadoop and a data warehouse to provide a responsive user experience for data slicing, aggregating, and modeling on 100% of data samples. A hybrid approach was implemented that uses Hadoop for ingestion and storage and a data warehouse for transformation, integration, and dimensional modeling to support both internal analysts and external customers. This hybrid approach meets the goals of being data and technology agnostic while providing speed for analytics.
Big Data: Working with Big SQL data from Spark Cynthia Saracco
Follow this hands-on lab to discover how Spark programmers can work with data managed by Big SQL, IBM's SQL interface for Hadoop. Examples use Scala and the Spark shell in a BigInsights 4.3 technical preview 2 environment.
Modernize Your Existing EDW with IBM Big SQL & Hortonworks Data PlatformHortonworks
Find out how Hortonworks and IBM help you address these challenges to enable success to optimize your existing EDW environment.
https://hortonworks.com/webinar/modernize-existing-edw-ibm-big-sql-hortonworks-data-platform/
This talk was held at the 11th meeting on April 7 2014 by Marcel Kornacker.
Impala (impala.io) raises the bar for SQL query performance on Apache Hadoop. With Impala, you can query Hadoop data – including SELECT, JOIN, and aggregate functions – in real time to do BI-style analysis. As a result, Impala makes a Hadoop-based enterprise data hub function like an enterprise data warehouse for native Big Data.
The document is a presentation about using Hadoop for analytic workloads. It discusses how Hadoop has traditionally been used for batch processing but can now also be used for interactive queries and business intelligence workloads using tools like Impala, Parquet, and HDFS. It summarizes performance tests showing Impala can outperform MapReduce for queries and scales linearly with additional nodes. The presentation argues Hadoop provides an effective solution for certain data warehouse workloads while maintaining flexibility, ease of scaling, and cost effectiveness.
InfoSphere BigInsights - Analytics power for Hadoop - field experienceWilfried Hoge
This document provides an overview and summary of InfoSphere BigInsights, an analytics platform for Hadoop. It discusses key features such as real-time analytics, storage integration, search, data exploration, predictive modeling, and application tooling. Case studies are presented on analyzing binary data and developing applications for transformation and analysis. Partnerships and certifications with other vendors are also mentioned. The document aims to demonstrate how BigInsights brings enterprise-grade features to Apache Hadoop and provides analytics capabilities for business users.
"Analyzing Twitter Data with Hadoop - Live Demo", presented at Oracle Open World 2014. The repository for the slides is in https://github.com/cloudera/cdh-twitter-example
This document provides an agenda and overview for a presentation on SQL on Hadoop. The presentation will cover various SQL on Hadoop technologies including Hive, HAWQ, Impala, SparkSQL, HBase with Phoenix, and Drill. It will also include an introduction, surveys to collect information from attendees, and discussions on networking and food. The hosts will provide background on their experience with big data and Hadoop.
Pivotal: Hadoop for Powerful Processing of Unstructured Data for Valuable Ins...EMC
Pivotal has setup and operationalized 1000 node Hadoop cluster called the Analytics Workbench. It takes special setup and skills to manage such a large deployment. This session shares how we set it up and how you will manage it.
Objective 1: Understand what it takes to operationalize a 1000-nodeHadoop cluster.
After this session you will be able to:
Objective 2: Understand how to set up and manage the day to day challenges of a large Hadoop deployments.
Objective 3: Have a view to the tools that are necessary to solve the challenges of managing the large Hadoop cluster.
Impala 2.0 - The Best Analytic Database for HadoopCloudera, Inc.
A look at why SQL access in Hadoop is critical and the benefits of a native Hadoop analytic database, what’s new with Impala 2.0 and some of the recent performance benchmarks, some common Impala use cases and production customer stories, and insight into what’s next for Impala.
TDC2017 | POA Trilha BigData - IBM BigSQL - Engine de consulta de dados de al...tdc-globalcode
Big SQL provides a concise ANSI SQL interface for analyzing data stored in Hadoop. It offers high performance, rich SQL functionality, and integration with data science tools. Big SQL preserves the open source foundations of Hive while improving performance through an optimized query execution engine and support for SQL standards.
Hive, Impala, and Spark, Oh My: SQL-on-Hadoop in Cloudera 5.5Cloudera, Inc.
Inefficient data workloads are all too common across enterprises - causing costly delays, breakages, hard-to-maintain complexity, and ultimately lost productivity. For a typical enterprise with multiple data warehouses, thousands of reports, and hundreds of thousands of ETL jobs being executed every day, this loss of productivity is a real problem. Add to all of this the complex handwritten SQL queries, and there can be nearly a million queries executed every month that desperately need to be optimized, especially to take advantage of the benefits of Apache Hadoop. How can enterprises dig through their workloads and inefficiencies to easily see which are the best fit for Hadoop and what’s the fastest path to get there?
Cloudera Navigator Optimizer is the solution - analyzing existing SQL workloads to provide instant insights into your workloads and turns that into an intelligent optimization strategy so you can unlock peak performance and efficiency with Hadoop. As the newest addition to Cloudera’s enterprise Hadoop platform, and now available in limited beta, Navigator Optimizer has helped customers profile over 1.5 million queries and ultimately save millions by optimizing for Hadoop.
Oracle Unified Information Architeture + Analytics by ExampleHarald Erb
Der Vortrag gibt zunächst einen Architektur-Überblick zu den UIA-Komponenten und deren Zusammenspiel. Anhand eines Use Cases wird vorgestellt, wie im "UIA Data Reservoir" einerseits kostengünstig aktuelle Daten "as is" in einem Hadoop File System (HDFS) und andererseits veredelte Daten in einem Oracle 12c Data Warehouse miteinander kombiniert oder auch per Direktzugriff in Oracle Business Intelligence ausgewertet bzw. mit Endeca Information Discovery auf neue Zusammenhänge untersucht werden.
Big SQL is IBM's massively parallel processing SQL engine for Apache Hadoop that allows users to run SQL queries against data stored in HDFS file formats like Parquet and ORC. It provides high performance, scalability, and integration with other data sources through query federation. Big SQL also offers advanced security, high availability, workload management, and monitoring capabilities for enterprise-grade analytics on large datasets.
http://www.learntek.org/product/big-data-and-hadoop/
http://www.learntek.org
Learntek is global online training provider on Big Data Analytics, Hadoop, Machine Learning, Deep Learning, IOT, AI, Cloud Technology, DEVOPS, Digital Marketing and other IT and Management courses. We are dedicated to designing, developing and implementing training programs for students, corporate employees and business professional.
Overview of big data & hadoop version 1 - Tony NguyenThanh Nguyen
Overview of Big data, Hadoop and Microsoft BI - version1
Big Data and Hadoop are emerging topics in data warehousing for many executives, BI practices and technologists today. However, many people still aren't sure how Big Data and existing Data warehouse can be married and turn that promise into value. This presentation provides an overview of Big Data technology and how Big Data can fit to the current BI/data warehousing context.
http://www.quantumit.com.au
http://www.evisional.com
Overview of Big data, Hadoop and Microsoft BI - version1Thanh Nguyen
Big Data and advanced analytics are critical topics for executives today. But many still aren't sure how to turn that promise into value. This presentation provides an overview of 16 examples and use cases that lay out the different ways companies have approached the issue and found value: everything from pricing flexibility to customer preference management to credit risk analysis to fraud protection and discount targeting. For the latest on Big Data & Advanced Analytics: http://mckinseyonmarketingandsales.com/topics/big-data
Similar to Big SQL Competitive Summary - Vendor Landscape (20)
Benchmarking SQL-on-Hadoop Systems: TPC or not TPC?Nicolas Morales
Abstract. Benchmarks are important tools to evaluate systems, as long as their results are transparent, reproducible and they are conducted with due diligence. Today, many SQL-on-Hadoop vendors use the data generators and the queries of existing TPC benchmarks, but fail to adhere to the rules, producing results that are not transparent. As the SQL-on-Hadoop movement continues to gain more traction, it is important to bring some order to this \wild west" of benchmarking. First, new rules and policies should be dened to satisfy the demands of the new generation SQL systems. The new benchmark evaluation schemes should be inexpensive, eective and open enough to embrace the variety of SQL-on-Hadoop systems and their corresponding vendors. Second, adhering to the new standards requires industry commitment and collaboration. In this paper, we discuss the problems we observe in the current practices of benchmarking, and present our proposal for bringing standardization in the SQL-on-Hadoop space.
InfoSphere BigInsights for Hadoop @ IBM Insight 2014Nicolas Morales
The document provides an agenda for the IBM Insight 2014 conference, listing sessions over multiple days that demonstrate and discuss IBM's Hadoop and big data technologies. Sessions will cover IBM InfoSphere BigInsights for managing Hadoop clusters, Big SQL for analyzing Hadoop data using SQL, and using Hadoop with IBM Cognos business intelligence and InfoSphere information integration tools. Hands-on labs are also scheduled to allow attendees to explore working with Hadoop, BigInsights, Big SQL, and big data analytics.
IBM Big SQL @ Insight 2014
Visit IBM Big SQL in the Information Management Demo room @ pedestal HD-01.
For more information:
- IBM Big SQL technology preview, visit http://ibm.biz/bigsqlpreview
- Hadoop and Big SQL, visit ibm.com/hadoop
- BigInsights Developer Community: https://developer.ibm.com/hadoop/
- IBM Insight 2014, visit ibm.com/software/events/insight
60 minutes in the cloud: Predictive analytics made easyNicolas Morales
This document discusses IBM's Bluemix platform as a service (PaaS) and its Analytics Warehouse Service. It provides an overview of Bluemix, describing how it allows developers to host and scale applications without managing infrastructure. The Analytics Warehouse Service is then introduced, highlighting its features like data loading, R integration for analysis, and built-in security. Finally, the document discusses how predictive analytics can be easily built using R and the bluR package to access data and push predictive models as Bluemix applications.
Challenges of Building a First Class SQL-on-Hadoop EngineNicolas Morales
Challenges of Building a First Class SQL-on-Hadoop Engine:
Why and what is Big SQL 3.0?
Overview of the challenges
How we solved (some of) them
Architecture and interaction with Hadoop
Query rewrite
Query optimization
Future challenges
BigInsights and Text Analytics.
As enterprises seek to gain operational efficiencies and competitive advantage through greater use of analytics, much of the new information they need to analyze is found in text documents and, increasingly, in a wide variety of social media sites and portals. A critical step in gaining insights from this information is extracting core data from huge volumes of text. That data is then available for downstream analytic, mining and machine learning tools. AQL (Annotator Query Language) is a powerful declarative, rule-based language for the extraction of information from text documents.
Social Data Analytics using IBM Big Data TechnologiesNicolas Morales
Distilling Insights from Social Media Using Big Data Technologies
Have you ever wondered what your customers are saying about you in Social media, and the impact it might be having on your business? This session will focus on how BigInsights and Big Data technologies can be used to glean useful and actionable insights from social media data.
You'll see how data can be ingested and prepped and do text analytics on social data in real time. Using Hadoop, we'll show you how you can store and analyze your large volume of historical social media data and reference data. This talk and demo will provide an introduction to text analytics and how it is used within the IBM Big Data platform for a social media solution.
The value of the fast growing class of big data technologies is the ability to handle high velocity and volumes of data. However, a lack of robust security and auditing capabilities are holding organizations back from fully using the potential of these systems. Learn how you can use Big Data technologies to help you meet this compliance and data protection challenge head on so you can return to innovating for competitive advantage.
Using InfoSphere Guardium and BigInsights, we'll show you how you can meet your Hadoop security, compliance and audit requirements.
Gain New Insights by Analyzing Machine Logs using Machine Data Analytics and BigInsights.
Half of Fortune 500 companies experience more than 80 hours of system down time annually. Spread evenly over a year, that amounts to approximately 13 minutes every day. As a consumer, the thought of online bank operations being inaccessible so frequently is disturbing. As a business owner, when systems go down, all processes come to a stop. Work in progress is destroyed and failure to meet SLA’s and contractual obligations can result in expensive fees, adverse publicity, and loss of current and potential future customers. Ultimately the inability to provide a reliable and stable system results in loss of $$$’s. While the failure of these systems is inevitable, the ability to timely predict failures and intercept them before they occur is now a requirement.
A possible solution to the problem can be found is in the huge volumes of diagnostic big data generated at hardware, firmware, middleware, application, storage and management layers indicating failures or errors. Machine analysis and understanding of this data is becoming an important part of debugging, performance analysis, root cause analysis and business analysis. In addition to preventing outages, machine data analysis can also provide insights for fraud detection, customer retention and other important use cases.
The History of Embeddings & Multimodal EmbeddingsZilliz
Frank Liu will walk through the history of embeddings and how we got to the cool embedding models used today. He'll end with a demo on how multimodal RAG is used.
How UiPath Discovery Suite supports identification of Agentic Process Automat...DianaGray10
📚 Understand the basics of the newly persona-based LLM-powered Agentic Process Automation and discover how existing UiPath Discovery Suite products like Communication Mining, Process Mining, and Task Mining can be leveraged to identify APA candidates.
Topics Covered:
💡 Idea Behind APA: Explore the innovative concept of Agentic Process Automation and its significance in modern workflows.
🔄 How APA is Different from RPA: Learn the key differences between Agentic Process Automation and Robotic Process Automation.
🚀 Discover the Advantages of APA: Uncover the unique benefits of implementing APA in your organization.
🔍 Identifying APA Candidates with UiPath Discovery Products: See how UiPath's Communication Mining, Process Mining, and Task Mining tools can help pinpoint potential APA candidates.
🔮 Discussion on Expected Future Impacts: Engage in a discussion on the potential future impacts of APA on various industries and business processes.
Enhance your knowledge on the forefront of automation technology and stay ahead with Agentic Process Automation. 🧠💼✨
Speakers:
Arun Kumar Asokan, Delivery Director (US) @ qBotica and UiPath MVP
Naveen Chatlapalli, Solution Architect @ Ashling Partners and UiPath MVP
UiPath Community Day Amsterdam: Code, Collaborate, ConnectUiPathCommunity
Welcome to our third live UiPath Community Day Amsterdam! Come join us for a half-day of networking and UiPath Platform deep-dives, for devs and non-devs alike, in the middle of summer ☀.
📕 Agenda:
12:30 Welcome Coffee/Light Lunch ☕
13:00 Event opening speech
Ebert Knol, Managing Partner, Tacstone Technology
Jonathan Smith, UiPath MVP, RPA Lead, Ciphix
Cristina Vidu, Senior Marketing Manager, UiPath Community EMEA
Dion Mes, Principal Sales Engineer, UiPath
13:15 ASML: RPA as Tactical Automation
Tactical robotic process automation for solving short-term challenges, while establishing standard and re-usable interfaces that fit IT's long-term goals and objectives.
Yannic Suurmeijer, System Architect, ASML
13:30 PostNL: an insight into RPA at PostNL
Showcasing the solutions our automations have provided, the challenges we’ve faced, and the best practices we’ve developed to support our logistics operations.
Leonard Renne, RPA Developer, PostNL
13:45 Break (30')
14:15 Breakout Sessions: Round 1
Modern Document Understanding in the cloud platform: AI-driven UiPath Document Understanding
Mike Bos, Senior Automation Developer, Tacstone Technology
Process Orchestration: scale up and have your Robots work in harmony
Jon Smith, UiPath MVP, RPA Lead, Ciphix
UiPath Integration Service: connect applications, leverage prebuilt connectors, and set up customer connectors
Johans Brink, CTO, MvR digital workforce
15:00 Breakout Sessions: Round 2
Automation, and GenAI: practical use cases for value generation
Thomas Janssen, UiPath MVP, Senior Automation Developer, Automation Heroes
Human in the Loop/Action Center
Dion Mes, Principal Sales Engineer @UiPath
Improving development with coded workflows
Idris Janszen, Technical Consultant, Ilionx
15:45 End remarks
16:00 Community fun games, sharing knowledge, drinks, and bites 🍻
Garbage In, Garbage Out: Why poor data curation is killing your AI models (an...Zilliz
Enterprises have traditionally prioritized data quantity, assuming more is better for AI performance. However, a new reality is setting in: high-quality data, not just volume, is the key. This shift exposes a critical gap – many organizations struggle to understand their existing data and lack effective curation strategies and tools. This talk dives into these data challenges and explores the methods of automating data curation.
Cracking AI Black Box - Strategies for Customer-centric Enterprise ExcellenceQuentin Reul
The democratization of Generative AI is ushering in a new era of innovation for enterprises. Discover how you can harness this powerful technology to deliver unparalleled customer value and securing a formidable competitive advantage in today's competitive market. In this session, you will learn how to:
- Identify high-impact customer needs with precision
- Harness the power of large language models to address specific customer needs effectively
- Implement AI responsibly to build trust and foster strong customer relationships
Whether you're at the early stages of your AI journey or looking to optimize existing initiatives, this session will provide you with actionable insights and strategies needed to leverage AI as a powerful catalyst for customer-driven enterprise success.
Discovery Series - Zero to Hero - Task Mining Session 1DianaGray10
This session is focused on providing you with an introduction to task mining. We will go over different types of task mining and provide you with a real-world demo on each type of task mining in detail.
Increase Quality with User Access Policies - July 2024Peter Caitens
⭐️ Increase Quality with User Access Policies ⭐️, presented by Peter Caitens and Adam Best of Salesforce. View the slides from this session to hear all about “User Access Policies” and how they can help you onboard users faster with greater quality.
The Challenge of Interpretability in Generative AI Models.pdfSara Kroft
Navigating the intricacies of generative AI models reveals a pressing challenge: interpretability. Our blog delves into the complexities of understanding how these advanced models make decisions, shedding light on the mechanisms behind their outputs. Explore the latest research, practical implications, and ethical considerations, as we unravel the opaque processes that drive generative AI. Join us in this insightful journey to demystify the black box of artificial intelligence.
Dive into the complexities of generative AI with our blog on interpretability. Find out why making AI models understandable is key to trust and ethical use and discover current efforts to tackle this big challenge.
DefCamp_2016_Chemerkin_Yury-publish.pdf - Presentation by Yury Chemerkin at DefCamp 2016 discussing mobile app vulnerabilities, data protection issues, and analysis of security levels across different types of mobile applications.
Keynote : Presentation on SASE TechnologyPriyanka Aash
Secure Access Service Edge (SASE) solutions are revolutionizing enterprise networks by integrating SD-WAN with comprehensive security services. Traditionally, enterprises managed multiple point solutions for network and security needs, leading to complexity and resource-intensive operations. SASE, as defined by Gartner, consolidates these functions into a unified cloud-based service, offering SD-WAN capabilities alongside advanced security features like secure web gateways, CASB, and remote browser isolation. This convergence not only simplifies management but also enhances security posture and application performance across global networks and cloud environments. Discover how adopting SASE can streamline operations and fortify your enterprise's digital transformation strategy.
Welcome to Cyberbiosecurity. Because regular cybersecurity wasn't complicated...Snarky Security
How wonderful it is that in our modern age, every bit of our biological data can be digitized, stored, and potentially pilfered by cyber thieves! Isn't it just splendid to think that while scientists are busy pushing the boundaries of biotechnology, hackers could be plotting the next big bio-data heist? This delightful scenario is brought to you by the ever-expanding digital landscape of biology and biotechnology, where the integration of computer science, engineering, and data science transforms our understanding and manipulation of biological systems.
While the fusion of technology and biology offers immense benefits, it also necessitates a careful consideration of the ethical, security, and associated social implications. But let's be honest, in the grand scheme of things, what's a little risk compared to potential scientific achievements? After all, progress in biotechnology waits for no one, and we're just along for the ride in this thrilling, slightly terrifying, adventure.
So, as we continue to navigate this complex landscape, let's not forget the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. After all, what could possibly go wrong?
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This document provides a comprehensive analysis of the security implications biological data use. The analysis explores various aspects of biological data security, including the vulnerabilities associated with data access, the potential for misuse by state and non-state actors, and the implications for national and transnational security. Key aspects considered include the impact of technological advancements on data security, the role of international policies in data governance, and the strategies for mitigating risks associated with unauthorized data access.
This view offers valuable insights for security professionals, policymakers, and industry leaders across various sectors, highlighting the importance of robust data protection measures and collaborative international efforts to safeguard sensitive biological information. The analysis serves as a crucial resource for understanding the complex dynamics at the intersection of biotechnology and security, providing actionable recommendations to enhance biosecurity in an digital and interconnected world.
The evolving landscape of biology and biotechnology, significantly influenced by advancements in computer science, engineering, and data science, is reshaping our understanding and manipulation of biological systems. The integration of these disciplines has led to the development of fields such as computational biology and synthetic biology, which utilize computational power and engineering principles to solve complex biological problems and innovate new biotechnological applications. This interdisciplinary approach has not only accelerated research and development but also introduced new capabilities such as gene editing and biomanufact
Keynote : AI & Future Of Offensive SecurityPriyanka Aash
In the presentation, the focus is on the transformative impact of artificial intelligence (AI) in cybersecurity, particularly in the context of malware generation and adversarial attacks. AI promises to revolutionize the field by enabling scalable solutions to historically challenging problems such as continuous threat simulation, autonomous attack path generation, and the creation of sophisticated attack payloads. The discussions underscore how AI-powered tools like AI-based penetration testing can outpace traditional methods, enhancing security posture by efficiently identifying and mitigating vulnerabilities across complex attack surfaces. The use of AI in red teaming further amplifies these capabilities, allowing organizations to validate security controls effectively against diverse adversarial scenarios. These advancements not only streamline testing processes but also bolster defense strategies, ensuring readiness against evolving cyber threats.
"Hands-on development experience using wasm Blazor", Furdak Vladyslav.pptxFwdays
I will share my personal experience of full-time development on wasm Blazor
What difficulties our team faced: life hacks with Blazor app routing, whether it is necessary to write JavaScript, which technology stack and architectural patterns we chose
What conclusions we made and what mistakes we committed
21. Oracle Big Data SQL – Exadata to Big Data Appliance
Oracle Big Data SQL = Remote query from Exadata to Oracle Big Data Appliance
Oracle Exadata
(Oracle 12 c Database)
Oracle Big Data
Appliance
(Cloudera Hadoop)
SQL query issued
Results returned